class_docstr: Provides StupidBackoff scores.
  
  In addition to initialization arguments from BaseNgramModel also requires
  a parameter alpha with which we scale the lower order probabilities.
  Note that this is not a true probability distribution as scores for ngrams
  of the same order do not sum up to unity.
code: def unmasked_score(self, word, context=None):
      if not context:
          # Base recursion
          return self.counts.unigrams.freq(word)
      counts = self.context_counts(context)
      word_count = counts[word]
      norm_count = counts.N()
      if word_count > 0:
          return word_count / norm_count
      else:
          return self.alpha * self.unmasked_score(word, context[1:])
  
imports: from nltk.lm.api import LanguageModel, Smoothing
  from nltk.lm.smoothing import AbsoluteDiscounting, KneserNey, WittenBell
label_desc: Use image node_id 2 for calling the StupidBackoff obj's underlying member method code with example usage: obj.unmasked_score(word, context) and returns: self, unknown, unknown